sv_utils¶
Copyright 20142019 Anthony Larcher
sv_utils
provides utilities to facilitate the work with SIDEKIT.

sv_utils.
check_file_list
(input_file_list, file_name_structure)[source]¶ Check the existence of a list of files in a specific directory Return a new list with the existing segments and a list of indices of those files in the original list. Return outputFileList and idx such that inputFileList[idx] = outputFileList
 Parameters
input_file_list – list of file names
file_name_structure – structure of the filename to search for
 Returns
a list of existing files and the indices of the existing files in the input list

sv_utils.
initialize_iv_extraction_eigen_decomposition
(ubm, T)[source]¶ Estimate matrices Q, D_bar_c and Tnorm, for approximation of the ivectors. For more information, refers to [Glembeck09]
 Parameters
ubm – Mixture object, Universal Background Model
T – Raw TotalVariability matrix
 Returns
Q: Q matrix as described in [Glembeck11] D_bar_c: matrices as described in [Glembeck11] Tnorm: total variability matrix prenormalized using the covariance of the UBM

sv_utils.
initialize_iv_extraction_fse
(ubm, T)[source]¶ Estimate matrices for approximation of the ivectors. For more information, refers to [Cumani13]
 Parameters
ubm – Mixture object, Universal Background Model
T – Raw TotalVariability matrix
 Returns
Q: Q matrix as described in [Glembeck11] D_bar_c: matrices as described in [Glembeck11] Tnorm: total variability matrix prenormalized using the covariance of the UBM

sv_utils.
initialize_iv_extraction_weight
(ubm, T)[source]¶ Estimate matrices W and T for approximation of the ivectors For more information, refers to [Glembeck09]
 Parameters
ubm – Mixture object, Universal Background Model
T – Raw TotalVariability matrix as a ndarray
 Returns
 W: fix matrix precomputed using the weights from the UBM and the
total variability matrix
 Tnorm: total variability matrix prenormalized using the covariance
of the UBM

sv_utils.
mean_std_many
(features_server, seg_list, in_context=False, num_thread=1)[source]¶ Compute the mean and standard deviation from a list of segments.
 Parameters
features_server –
seg_list – list of file names with start and stop indices
in_context –
num_thread –
 Returns
a tuple of three values, the number of frames, the mean and the variance

sv_utils.
read_svm
(svm_file_name)[source]¶ Read SVM model in PICKLE format
 Parameters
svm_file_name – name of the file to read from
 Returns
a tupple of weight and biais

sv_utils.
save_svm
(svm_file_name, w, b)[source]¶ Save SVM weights and bias in PICKLE format
 Parameters
svm_file_name – name of the file to write
w – weight coefficients of the SVM to store
b – biais of the SVM to store

sv_utils.
segment_mean_std_hdf5
(input_segment, in_context=False)[source]¶ Compute the sum and square sum of all features for a list of segments. Input files are in HDF5 format
 Parameters
input_segment – list of segments to read from, each element of the list is a tuple of 5 values, the filename, the index of thefirst frame, index of the last frame, the number of frames for the left context and the number of frames for the right context
in_context –
 Returns
a tuple of three values, the number of frames, the sum of frames and the sum of squares